Collaborative Research: WCR: Incorporation of Model Bias and Uncertainty in Land Surface Hydrologic Flux Prediction Using a Data Assimilation Framework

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0333133MargulisA major weakness of current data assimilation algorithms is that they typically do not account for model errors or assume that the model errors are unbiased. However biases are inevitably introduced in the models due to poorly specified forcing and parameters. Understanding the variance and memory of these errors is key to properly including their representation in data assimilation frameworks and will ultimately lead to improving our ability to predict variations in hydrologic fluxes.In this project we will focus on incorporating biases and other errors that can occur as a result of erroneous forcing and parameterizations in a data assimilation framework. The first tasks in our proposed research project will consist of numerical simulations to characterize the types of errors that are specific to land surface hydrologic modeling. The goal is to develop parsimonious probabilistic error models (that account for biases and other model errors) that are necessary inputs to data assimilation algorithms. Variational data assimilation experiments will then be performed to design an observing system that can ultimately be incorporated into a real-time data assimilation framework and is capable of including these parameterizations of model biases and errors. The final set of research tasks will be to incorporate the error models and observing system into a real-time (operational) data assimilation framework using the Ensemble Kalman Filter.The expected results of this study include the characterization of those errors that are caused by the inevitable misspecification of surface forcing and model parameters and incorporate them into land data assimilation systems.